Date: Wed, 20 Nov 1996 19:16:38 GMT
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<HEAD><TITLE> Learning Case Adaptation for Case-Based Reasoning </TITLE></HEAD>
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<H2> Learning Case Adaptation for Case-Based Reasoning </H2>



<Strong>Description: </Strong> 

<Blockquote> A central research challenge for case-based reasoning
(CBR) is developing methods for adapting retrieved solutions to solve
new problems.  Current adaptation methods rely on hand-coded
task-specific information, placing considerable burden on the system
developer.  This project studies how useful case adaptation knowledge
can automatically be learned and reused.  It models how CBR systems,
starting with only domain- and task-independent adaptation
information, can augment that general knowledge by learning
domain-specific information from the adaptations they perform.
Learning is done by storing traces of the solution process used in
successful adaptations, to enable those traces to be reused for future
adaptations.  This method allows CBR systems to learn not only new
solutions, but also how to make better use of existing solutions in their
memories.
 </Blockquote>
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<Strong> Associated Faculty: </Strong>  
<A NAME HREF="http://www.cs.indiana.edu/hyplan/leake.html">David B. Leake</A>
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<Strong> Associated Graduate Students: </Strong>  
<!WA0><A HREF="http://www.cs.indiana.edu/hyplan/akinley.html">
Andrew  Kinley</A>, <!WA1><A HREF="http://www.cs.indiana.edu/hyplan/davwils.html">David Wilson</A>.
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<Strong> Support: </Strong> 
This research is supported by
grant IRI-9409348 from the 
<A NAME HREF="http://www.nsf.gov/cise/iris/knowlmdl.htm"> Knowledge
Models and Cognitive Systems Program </A> of the 
<A NAME HREF="http://www.nsf.gov/cise/iris/iris.htm"> IRIS division </A> 
of the 
<A NAME HREF="http://www.nsf.gov/"> National Science Foundation </A>.
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<Strong> For more information about CBR research at Indiana,</Strong> <!WA2><A
HREF="http://www.cs.indiana.edu/hyplan/leake/cbr/overview.html">  
click here </A>
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